RE: weighting observed data

From: "Sale, Mark" <ms93267@glaxowellcome.com> Subject: RE: weighting observed data Date: Mon, 5 Jun 2000 07:51:53 -0400 You certainly can have a different residual error for the two samples. Just have an indicator variable in the data set (IND = 1 if plasma, 0 if dialysate). Then the error is: ET = IND*EPS(1) + (1-IND)*EPS(2) But, that may not solve the problem. When you have a lot more data from one site/assay, that site/assay will drive the other model, i.e., the pk part will be made the best fit the dialysate data, not the pk data. I ran into this same problem with QT interval relating to plasma concentration. It is my view that the model should reflect biological causation whenever possible. In the QT case, clearly plasma concentrations drive the QT interval. I don't want QT interval driving plasma concentration. So, I fit the plasma concentration first, fix that part (output individual pk parameters using post hoc), then fit the QT part (merge the individual pk parameters into the data set for QT). In your case, I'd consider this approach. The dialysate concentration should, mechanistically, be driven by the plasma concentration, not vise versa. So, fit the pk first, fix that, then fit the dialysate model. Mark